1. Field of the Invention
The present invention relates to a vehicle body slip angle-estimating device and method for estimating a vehicle body slip angle with an algorithm using a nonlinear model, and an engine control unit.
2. Description of the Related Art
Conventionally, there has been proposed a vehicle body slip angle-estimating device that estimates a vehicle body slip angle e.g. in Japanese Laid-Open Patent Publication (Kokai) No. H07-25327. This vehicle body slip angle-estimating device is provided with a yaw rate sensor for detecting the yaw rate of a vehicle, a steering angle sensor for detecting the steering angle of the steering of the vehicle, and a vehicle speed sensor for detecting a vehicle speed.
In the vehicle body slip angle-estimating device, as described hereinafter, a vehicle body slip angle is estimated with an algorithm using a neural network model as a nonlinear model. First, the amount of change in an estimated value of the vehicle body slip angle is calculated using a neural network model shown in FIG. 13 of the publication to which are input the immediately preceding estimated value of the vehicle body slip angle, a yaw rate, the steering angle of front wheels, that of rear wheels, the reciprocal of the vehicle speed, and the reciprocal of the square of the vehicle speed, and from which is output the amount of change in the estimated value of the vehicle body slip angle. Then, the current estimated value of the vehicle body slip angle is calculated by adding the immediately preceding estimated value to the amount of change in the estimated value of the vehicle body slip angle.
The weights of an intermediate layer and an output layer of the FIG. 13 neural network model are learned in the following manner: Using a neural network model shown in FIG. 14 of the publication to which are input the yaw rate, the steering angle of the front wheels, that of the rear wheels, the reciprocal of the vehicle speed, the reciprocal of the square of the vehicle speed, a frictional resistance, all these inputs being obtained when the vehicle is actually traveling on a curved road, and from which are output the estimated value of the vehicle body slip angle and an estimated yaw rate, the weights of the intermediate layer and the output layer are learned such that the estimated value of the vehicle body slip angle and the estimated yaw rate become equal to the measured value of the vehicle body slip angle and the measured value of the yaw rate, respectively.
According to the above-described conventional vehicle body slip angle-estimating device, the vehicle body slip angle is estimated with the algorithm using the neural network model, and the learned values of the weights of the respective layers of the neural network model are calculated based on the measured values of the yaw rate, the steering angle of the front wheels, and so forth, all of which are obtained during traveling of the vehicle on a curved road. Therefore, when the vehicle is in a traveling state which is to be associated with measured values of the above-mentioned parameters occurring with a low frequency, such as a limit turning traveling state in which an excessively large vehicle body slip angle is temporarily generated, the calculation accuracy of the learned values of the weights is low and hence the estimation accuracy of the vehicle body slip angle is also degraded.